Technical Product Manager for the AI Platform.
Position Overview:

We are seeking a Technical Product Manager for the AI Platform to lead the strategic planning, technical coordination, and execution across our enterprise AI/ML and Agentic platform ecosystem.

In this role, you will bridge deep engineering capability with product management rigor to convert enterprise AI strategy into actionable, dependency-aware roadmaps.

You will oversee a unified platform that powers AI agents, Generative AI, traditional ML, and shared foundational capabilities.

This role requires a technical mindset with a background in software development, microservices architecture, and hands-on understanding of APIs, protocol integrations (such as Model Context Protocol - MCP), and End-to-End (E2E) testing.

You will partner with Engineering Leads, AI Architects, QA, Security, Compliance, and business stakeholders to deliver reliable, secure, observable, and cost-effective AI platform products that accelerate time-to-value across the organization.

What will you do?
Platform Roadmap & Strategy: Translate AI vision into a multi-quarter, dependency-aware platform roadmap; prioritize platform capabilities based on measurable business impact, cost-efficiency, technical risk, and architectural readiness.
Technical Backlog & Requirements Definition: Write high-quality Epics, User Stories, and Technical Specifications tied to clear outcomes, success metrics, and robust Definition of Ready (DoR) / Definition of Done (DoD).
Architecture & Systems Alignment: Partner with architects and engineering squads to define platform-level features around microservices, REST/gRPC APIs, backend build services, Model Context Protocol (MCP) integrations, and LLM orchestration layers.
Testing & Quality Assurance Oversight: Deeply understand and review test cases, define acceptance criteria, and ensure comprehensive End-to-End (E2E) test coverage across AI pipelines, agentic workflows, model evaluations, and integrations.




Dependency & Execution Management: Identify, map, and resolve cross-team technical dependencies across multiple engineering squads, data platforms, and external vendor/partner APIs.
Release & Lifecycle Management: Own platform release planning, environment readiness, promotion criteria, rollout/rollback strategies, and release notes across staging and production environments.
Governance, Security & Compliance: Collaborate with Security, Data Governance, and Compliance teams to enforce data boundaries, access controls, auditability, and guardrail policies within the AI platform.
Observability & Cost Management: Track platform reliability (SLIs/SLOs), compute/token spend, and usage metrics using enterprise monitoring tools; drive Fin

Ops optimizations for model inference and infrastructure.
Process Standardization: Champion best practices in Agile/Scrum, SDLC discipline, Jira workflows, and CI/CD promotion standards across AI engineering squads.

Qualifications:
• Education & Experience:
~ Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or a related technical field.
~5+ years of experience as a Technical Product Manager, Technical Product Owner, or Technical Lead for platform, infrastructure, developer tools, or distributed software systems.
~ Prior hands-on experience as a Software Engineer, Backend Developer, or AI/ML Engineer (strongly preferred).

• Software Engineering & Technical Stack:
Deep understanding of the full Software Development Life Cycle (SDLC) and modern engineering workflows (CI/CD, Git, automated builds).




Knowledge and understanding of Model Context Protocol (MCP) for connecting AI models to external tools and data sources.
Practical capability to read/review code, evaluate test cases, and validate End-to-End (E2E) functional and integration tests.
• AI & Machine Learning Foundations:
Solid knowledge of AI/ML fundamentals, LLMs, prompt frameworks, agentic workflows, RAG architectures, vector databases, and evaluation pipelines (Evals).
Familiarity with AI guardrails, model routing/gateways, token cost optimization, and inference latency management.
• Methodologies & Tools:
~ Advanced proficiency with Jira, Confluence, and Agile/Scrum frameworks.

Skills:
Hard Skills
Technical Product Management (Platform/APIs)
AI / LLM / Agentic Systems & ML Foundations
Model Context Protocol (MCP) & Agent Tooling
Agile / Scrum / Backlog Refinement (Jira, Confluence)
Observability & SLI/SLO Monitoring
Soft Skills
Technical Systems Thinking & Architectural Empathy
Strategic Prioritization & Trade-off Analysis
Cross-Functional Leadership & Stakeholder Management
Complex Dependency & Risk Management
Analytical & Data-Driven Problem Solving
Clear Technical Communication (Engineering to Executive)
Detail Oriented & Quality-Driven Execution
Technical Expertise
AI Platform Management
Agentic Architectures & Generative AI Infrastructure
Software Engineering & API Ecosystems

Nice to have:
Familiarity with Microservices Architecture & API Design (REST/gRPC)
Familiarity with Cloud-Native Architecture (AWS, Docker, Kubernetes) SDLC & CI/CD Pipelines.
Familiarity with in cloud-native environments (AWS/Azure/GCP, Docker, Kubernetes/EKS).
Familiarity with telemetry and monitoring tools (Grafana, Prometheus, Cloud

Watch, Datadog) for SLO and performance tracking.
Experience working in regulated or high-governance environments (e.g., HIPAA, SOC2, audit trails).

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